Application-specific clustering in wireless sensor networks using combined fuzzy firefly algorithm and random forest

Abstract

Clustering in wireless sensor networks (WSNs) has proved to be one of the most efficient ways to hierarchically organize the network topology for the purposes of load-balancing and elongating the network lifetime. However, achieving optimal clustering in WSNs is an NP-hard problem, and consequently, heuristics and metaheuristics have been widely adopted. In this paper, a combined clustering technique based on a fuzzy-firefly algorithm (FFA) and random forest (RF), shortly named as: FFA-RF, is presented as an application-specific routing protocol for WSNs. Our FFA-RF protocol entails offline tuning and online routing phases: the offline phase consists of data collection using FFA, training and test of the RF, while the online phase is the actual application of the FFA-RF model to new network instances. In the offline phase, we construct an optimized fuzzy inference system based on FFA and apply it on different network topologies, to collect a comprehensive dataset. We then divide the resulting dataset into training and test sets to train and test the RF model. In the online phase, the trained RF model is used as an online clustering algorithm to estimate the fuzzy priority factor of the nodes for being cluster heads (CHs) in new network instances. To increase the generalizability of the RF for different configurations, node features as well as application features are used as inputs of the RF model. Simulation results for different network topologies demonstrate the superiority of the proposed FFA-RF protocol in prolonging the application-specific lifetime when compared against existing crisp heuristic, fuzzy heuristic, metaheuristic, and combined fuzzy-metaheuristic protocols.